Veriff vs Satlas

Veriff verifies government IDs, selfies, and business records so companies can onboard users, run KYC and AML checks, and block fraud in about six seconds. Its decision engine analyzes behavioral data across each session and is trusted by more than 3,000 companies worldwide, including Instacart, Western Union, Wise, and Monzo. Automated identity checks report roughly 99.6% accuracy.

Many verification vendors license document forensics, liveness, and face-match models from third parties and rebrand the stack. Veriff builds its decision engine, liveness matching, and document forensics in-house, so one team owns accuracy when a case fails review. AML and database checks use named specialist partners listed in its sub-processor documentation rather than hidden behind the brand.

The platform combines document checks, biometric matching, passive liveness detection, and database cross-referencing into one workflow. It supports more than 12,500 government-issued identity documents across 230+ countries and territories, with end-user flows available in 50 languages. Beyond onboarding, Veriff covers proof of address, age validation, AML screening, business verification across 300+ jurisdictions, and ongoing biometric re-authentication.

Financial services, marketplaces, iGaming, mobility, and HR teams use Veriff for KYC, KYB, age assurance, and account reverification at scale. Fraud tools like CrossLinks, FaceBlock, and session video recording help teams spot synthetic identities and repeat offenders across the shared verification network.

Satlas is an open geospatial platform from the Allen Institute for AI (Ai2) that uses computer vision models to analyze public Sentinel-2 satellite imagery and produce global, monthly-updated datasets. It lets researchers, environmental scientists, and geospatial analysts explore how the planet is changing through an interactive map covering marine infrastructure, renewable energy infrastructure, and tree cover.

The platform processes freely available satellite images with AI models pre-trained on SatlasPretrain, a remote sensing dataset with over 30 TB of imagery and 302 million labels. Users can compare side-by-side or timelapse views of offshore wind farms, solar installations, deforestation, and other land-use changes dating back to January 2016.

All geospatial data products, model weights, and training labels are freely downloadable under the ODC-BY license. Satlas also includes a super-resolution module that enhances low-resolution Sentinel-2 imagery to produce higher-detail global views, with open-source code available on GitHub.